Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization
Historically, liquefaction has caused a number of earthquake-related risks. When granular soils get saturated, liquefaction may occur during an earthquake, which can have devastating effects. Therefore, it is essential, especially in the context of civil and structural project planning, to have the...
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Pouyan Press
2025-04-01
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| Series: | Journal of Soft Computing in Civil Engineering |
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| Online Access: | https://www.jsoftcivil.com/article_197818_dba95ffa6c9605e6a74093b693c3b7ab.pdf |
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| author | Long Tsang Mahdi Akbari Pouyan Fakharian |
| author_facet | Long Tsang Mahdi Akbari Pouyan Fakharian |
| author_sort | Long Tsang |
| collection | DOAJ |
| description | Historically, liquefaction has caused a number of earthquake-related risks. When granular soils get saturated, liquefaction may occur during an earthquake, which can have devastating effects. Therefore, it is essential, especially in the context of civil and structural project planning, to have the capacity to precisely predict soil liquefaction potential. Therefore, the stacked ensemble-learning model with Bayesian optimization (BO-stacking) is introduced to make predictions of soil liquefaction more accurate. It was constructed utilizing primary algorithms like decision trees, support vector machines, and k-nearest neighbors, as well as secondary algorithms like the random forest algorithm. A Bayesian optimization method is also used to improve the accuracy of the predictions of soil liquefaction by adjusting the hyperparameters of these four classification algorithms. Information gain technique also was used for input selection. The results show that BO-stacking outperformed single prediction models. The testing accuracy and ACU of this model was 0.913 and 0.992, respectively. This study indicates that BO-stacking is a feasible alternative to established techniques for predicting soil liquefaction. In addition, the results of this study indicate that the BO and stacking approaches are effective in training the prediction model when used in conjunction. |
| format | Article |
| id | doaj-art-b3ab2b77bf36448ab736b66ba2ffd356 |
| institution | Kabale University |
| issn | 2588-2872 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-b3ab2b77bf36448ab736b66ba2ffd3562025-08-20T03:34:42ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722025-04-0192325510.22115/scce.2024.453006.1860197818Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian OptimizationLong Tsang0Mahdi Akbari1Pouyan Fakharian2Geofirst Pty Ltd., 2/7 Luso Drive, Unanderra, NSW 2526, AustraliaFaculty of Civil Engineering, Semnan University, Semnan, IranPh.D. Candidate, Member of Scientific Society of Civil Engineering Students, Faculty of Civil Engineering, Semnan University, Semnan, IranHistorically, liquefaction has caused a number of earthquake-related risks. When granular soils get saturated, liquefaction may occur during an earthquake, which can have devastating effects. Therefore, it is essential, especially in the context of civil and structural project planning, to have the capacity to precisely predict soil liquefaction potential. Therefore, the stacked ensemble-learning model with Bayesian optimization (BO-stacking) is introduced to make predictions of soil liquefaction more accurate. It was constructed utilizing primary algorithms like decision trees, support vector machines, and k-nearest neighbors, as well as secondary algorithms like the random forest algorithm. A Bayesian optimization method is also used to improve the accuracy of the predictions of soil liquefaction by adjusting the hyperparameters of these four classification algorithms. Information gain technique also was used for input selection. The results show that BO-stacking outperformed single prediction models. The testing accuracy and ACU of this model was 0.913 and 0.992, respectively. This study indicates that BO-stacking is a feasible alternative to established techniques for predicting soil liquefaction. In addition, the results of this study indicate that the BO and stacking approaches are effective in training the prediction model when used in conjunction.https://www.jsoftcivil.com/article_197818_dba95ffa6c9605e6a74093b693c3b7ab.pdfearthquakesoil liquefaction potentialmachine learningbayesian optimizationstacked ensemble |
| spellingShingle | Long Tsang Mahdi Akbari Pouyan Fakharian Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization Journal of Soft Computing in Civil Engineering earthquake soil liquefaction potential machine learning bayesian optimization stacked ensemble |
| title | Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization |
| title_full | Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization |
| title_fullStr | Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization |
| title_full_unstemmed | Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization |
| title_short | Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization |
| title_sort | prediction of soil liquefaction using a multi algorithm technique stacking ensemble techniques and bayesian optimization |
| topic | earthquake soil liquefaction potential machine learning bayesian optimization stacked ensemble |
| url | https://www.jsoftcivil.com/article_197818_dba95ffa6c9605e6a74093b693c3b7ab.pdf |
| work_keys_str_mv | AT longtsang predictionofsoilliquefactionusingamultialgorithmtechniquestackingensembletechniquesandbayesianoptimization AT mahdiakbari predictionofsoilliquefactionusingamultialgorithmtechniquestackingensembletechniquesandbayesianoptimization AT pouyanfakharian predictionofsoilliquefactionusingamultialgorithmtechniquestackingensembletechniquesandbayesianoptimization |